Assistant/Associate Professor

SUPERVISORY AUTHORITY:

The candidate will supervise postdocs, students, research assistants, or lab interns conducting projects. 

NATURE AND SCOPE:

The successful candidate will be housed in the Department of Pediatrics and Child Health in the College of Medicine (COM) and will develop an innovative, extramurally-funded research program applying AI techniques and advanced data science to questions at the intersection of environmental health, variation in health outcomes across populations, and clinical allergies and asthma care.

Research expertise in social and environmental determinants of health employing  artificial intelligence methodologies is required.

The faculty member will lead interdisciplinary research at the intersection of computational health, AI, GIS, and medical data analytics, utilizing data science and data engineering approaches to convert unstructured data into structured formats suitable for large-scale analysis. This is an exciting opportunity to work with a cross-disciplinary team to build computational infrastructure to mobilize data. Experience designing systems to capture and structure unstructured electronic health record (EHR) data is required, and the design and maintenance of efficient, secure databases.

The successful candidate will publish in high-impact journals, present at national and international conferences, and pursue competitive external funding. The candidate will partner with another AI Cluster hire, housed in the School of Social Work and the College of Arts and Sciences’ Department of Earth, Environment, and Equity (E3), to develop interdisciplinary coursework and advance collaborative research applying AI and machine learning to clinical, environmental, and behavioral datasets.

Preferred screening deadline of March 31, 2026, and will be considered until position is filled. Be sure to submit your cv, research statement, teaching statement, and letters of recommendation.

PRINCIPAL ACCOUNTABILITIES:

• Lead interdisciplinary research at the intersection of computational health, AI, GIS, and medical data analysis, leveraging data science and data engineering approaches to convert unstructured data into structured, analyzable formats.

• Apply machine learning models to analyze and interpret complex environmental and health data, with a focus on digital biomarker discovery.

• Design systems to capture unstructured EHR data; demonstrate proficiency in Python or R, structured query language (SQL), cloud-based solutions (e.g., Microsoft Azure), and database design and management. 

• Publish research findings in high-impact journals, present at conferences, and pursue external funding through competitive grant submissions.

• Partner with other AI cluster faculty to develop interdisciplinary coursework, including responsible and ethical AI in healthcare.

• Contribute to teaching and mentoring medical, graduate, and undergraduate students.

CORE COMPETENCIES:    

• M.D., Ph.D., M.D./Ph.D., or M.D./M.S. or equivalent terminal degree in Environmental Health, Data Science, Epidemiology, Biomedical Informatics, Computer Science, or a related field.

• Strong expertise in AI, machine learning, and natural language processing for data extraction and analysis.

• Demonstrated experience working with electronic health records (EHRs) and digital biomarker data analysis.

• Excellent written and oral communication skills, with a proven track record of publishing high-quality research.

• Ability to work collaboratively in an interdisciplinary research environment.

MINIMUM REQUIREMENTS:

• Experience with large-scale health data analysis, including the use of big data technologies.

• Knowledge of regulatory and ethical considerations related to health data, including electronic health records governance.

• Familiarity with public health datasets, environmental exposure assessment, and population health outcomes.

• Experience in database management, data visualization, and bioinformatics tools.

• Teaching experience in environmental health or related fields is desirable but not required.